{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "# '0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313'\n",
    "# https://waditu.com/document/2?doc_id=25\n",
    "ts.set_token('0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exchange</th>\n",
       "      <th>cal_date</th>\n",
       "      <th>is_open</th>\n",
       "      <th>pretrade_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180901</td>\n",
       "      <td>0</td>\n",
       "      <td>20180831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180902</td>\n",
       "      <td>0</td>\n",
       "      <td>20180831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180908</td>\n",
       "      <td>0</td>\n",
       "      <td>20180907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180909</td>\n",
       "      <td>0</td>\n",
       "      <td>20180907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180915</td>\n",
       "      <td>0</td>\n",
       "      <td>20180914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180916</td>\n",
       "      <td>0</td>\n",
       "      <td>20180914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180922</td>\n",
       "      <td>0</td>\n",
       "      <td>20180921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180923</td>\n",
       "      <td>0</td>\n",
       "      <td>20180921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180924</td>\n",
       "      <td>0</td>\n",
       "      <td>20180921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180929</td>\n",
       "      <td>0</td>\n",
       "      <td>20180928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20180930</td>\n",
       "      <td>0</td>\n",
       "      <td>20180928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SSE</td>\n",
       "      <td>20181001</td>\n",
       "      <td>0</td>\n",
       "      <td>20180928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   exchange  cal_date  is_open pretrade_date\n",
       "0       SSE  20180901        0      20180831\n",
       "1       SSE  20180902        0      20180831\n",
       "2       SSE  20180908        0      20180907\n",
       "3       SSE  20180909        0      20180907\n",
       "4       SSE  20180915        0      20180914\n",
       "5       SSE  20180916        0      20180914\n",
       "6       SSE  20180922        0      20180921\n",
       "7       SSE  20180923        0      20180921\n",
       "8       SSE  20180924        0      20180921\n",
       "9       SSE  20180929        0      20180928\n",
       "10      SSE  20180930        0      20180928\n",
       "11      SSE  20181001        0      20180928"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pro.query('trade_cal', exchange='', start_date='20180901', end_date='20181001', fields='exchange,cal_date,is_open,pretrade_date', is_open='0')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res = df.to_sql('stock_basic', engine_ts, index=False, if_exists='append', chunksize=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ts_code           trade_time   open  close   high    low      vol  \\\n",
      "0    600000.SH  2020-01-08 15:00:00  12.32  12.32  12.32  12.32  1485837   \n",
      "1    600000.SH  2020-01-08 14:59:00  12.32  12.32  12.32  12.32        0   \n",
      "2    600000.SH  2020-01-08 14:58:00  12.32  12.32  12.32  12.32    22000   \n",
      "3    600000.SH  2020-01-08 14:57:00  12.32  12.32  12.33  12.32   266196   \n",
      "4    600000.SH  2020-01-08 14:56:00  12.32  12.32  12.33  12.32   265829   \n",
      "..         ...                  ...    ...    ...    ...    ...      ...   \n",
      "477  600000.SH  2020-01-07 09:34:00  12.55  12.55  12.57  12.55   406300   \n",
      "478  600000.SH  2020-01-07 09:33:00  12.51  12.55  12.55  12.50   427336   \n",
      "479  600000.SH  2020-01-07 09:32:00  12.51  12.50  12.52  12.50   350500   \n",
      "480  600000.SH  2020-01-07 09:31:00  12.51  12.50  12.51  12.50   335700   \n",
      "481  600000.SH  2020-01-07 09:30:00  12.51  12.51  12.51  12.51   346000   \n",
      "\n",
      "         amount trade_date  pre_close  \n",
      "0    18305512.0   20200108      12.32  \n",
      "1           0.0   20200108      12.32  \n",
      "2      271040.0   20200108      12.32  \n",
      "3     3280959.0   20200108      12.32  \n",
      "4     3275157.0   20200108      12.32  \n",
      "..          ...        ...        ...  \n",
      "477   5102477.0   20200107      12.55  \n",
      "478   5351921.0   20200107      12.50  \n",
      "479   4385232.0   20200107      12.50  \n",
      "480   4198674.0   20200107      12.51  \n",
      "481   4328460.0   20200107        NaN  \n",
      "\n",
      "[482 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "df = ts.pro_bar(ts_code='600000.SH',\n",
    "                    freq='1min', \n",
    "                    start_date='2020-01-07 09:00:00', \n",
    "                    end_date='2020-01-08 17:00:00')\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>industry</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>000001</td>\n",
       "      <td>平安银行</td>\n",
       "      <td>深圳</td>\n",
       "      <td>银行</td>\n",
       "      <td>19910403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002</td>\n",
       "      <td>万科A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>全国地产</td>\n",
       "      <td>19910129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004</td>\n",
       "      <td>国华网安</td>\n",
       "      <td>深圳</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>19910114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>000005</td>\n",
       "      <td>ST星源</td>\n",
       "      <td>深圳</td>\n",
       "      <td>环境保护</td>\n",
       "      <td>19901210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>000006</td>\n",
       "      <td>深振业A</td>\n",
       "      <td>深圳</td>\n",
       "      <td>区域地产</td>\n",
       "      <td>19920427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4492</th>\n",
       "      <td>688819.SH</td>\n",
       "      <td>688819</td>\n",
       "      <td>天能股份</td>\n",
       "      <td>浙江</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>20210118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4493</th>\n",
       "      <td>688981.SH</td>\n",
       "      <td>688981</td>\n",
       "      <td>中芯国际</td>\n",
       "      <td>上海</td>\n",
       "      <td>半导体</td>\n",
       "      <td>20200716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494</th>\n",
       "      <td>834765.BJ</td>\n",
       "      <td>834765</td>\n",
       "      <td>美之高(测试)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>20211008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4495</th>\n",
       "      <td>836433.BJ</td>\n",
       "      <td>836433</td>\n",
       "      <td>大唐药业(测试)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>20211008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4496</th>\n",
       "      <td>689009.SH</td>\n",
       "      <td>689009</td>\n",
       "      <td>九号公司-WD</td>\n",
       "      <td>北京</td>\n",
       "      <td>摩托车</td>\n",
       "      <td>20201029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4497 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ts_code  symbol      name  area industry list_date\n",
       "0     000001.SZ  000001      平安银行    深圳       银行  19910403\n",
       "1     000002.SZ  000002       万科A    深圳     全国地产  19910129\n",
       "2     000004.SZ  000004      国华网安    深圳     软件服务  19910114\n",
       "3     000005.SZ  000005      ST星源    深圳     环境保护  19901210\n",
       "4     000006.SZ  000006      深振业A    深圳     区域地产  19920427\n",
       "...         ...     ...       ...   ...      ...       ...\n",
       "4492  688819.SH  688819      天能股份    浙江     电气设备  20210118\n",
       "4493  688981.SH  688981      中芯国际    上海      半导体  20200716\n",
       "4494  834765.BJ  834765   美之高(测试)  None     None  20211008\n",
       "4495  836433.BJ  836433  大唐药业(测试)  None     None  20211008\n",
       "4496  689009.SH  689009   九号公司-WD    北京      摩托车  20201029\n",
       "\n",
       "[4497 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pro.query('stock_basic', exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('F:\\HQData\\申万指数\\SwClass\\\\tushare_stock.csv', encoding='utf_8_sig', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfone = pro.daily(ts_code='000001.SZ', start_date='20180701', end_date='20180718')\n",
    "\n",
    "#多个股票\n",
    "dfmultiple = pro.daily(ts_code='000001.SZ,600000.SH', start_date='20180701', end_date='20180718')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfone.index = dfone.trade_date\n",
    "newcolumns =['symbol','Date','Open','High','Low','Close','Pre_Close','Change','Pct_chg', 'Volumn','Amount']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Pre_Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>Pct_chg</th>\n",
       "      <th>Volumn</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20180718</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180718</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.85</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.72</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>525152.77</td>\n",
       "      <td>460697.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180717</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180717</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.72</td>\n",
       "      <td>8.73</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>375356.33</td>\n",
       "      <td>326396.994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180716</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180716</td>\n",
       "      <td>8.85</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.88</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>689845.58</td>\n",
       "      <td>603427.713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180713</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180713</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.94</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>603378.21</td>\n",
       "      <td>535401.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180712</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180712</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.97</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1140492.31</td>\n",
       "      <td>1008658.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180711</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180711</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.83</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.98</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>851296.70</td>\n",
       "      <td>744765.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180710</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180710</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8.98</td>\n",
       "      <td>9.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>896862.02</td>\n",
       "      <td>803038.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180709</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180709</td>\n",
       "      <td>8.69</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.68</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.66</td>\n",
       "      <td>0.37</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1409954.60</td>\n",
       "      <td>1255007.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180706</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180706</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.70</td>\n",
       "      <td>988282.69</td>\n",
       "      <td>852071.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180705</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180705</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.61</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>835768.77</td>\n",
       "      <td>722169.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180704</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180704</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.67</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>711153.37</td>\n",
       "      <td>617278.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180703</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180703</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.67</td>\n",
       "      <td>8.61</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1274838.57</td>\n",
       "      <td>1096657.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180702</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180702</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.05</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.09</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-5.28</td>\n",
       "      <td>1315520.13</td>\n",
       "      <td>1158545.868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             symbol      Date  Open  High   Low  Close  Pre_Close  Change  \\\n",
       "Date                                                                        \n",
       "20180718  000001.SZ  20180718  8.75  8.85  8.69   8.70       8.72   -0.02   \n",
       "20180717  000001.SZ  20180717  8.74  8.75  8.66   8.72       8.73   -0.01   \n",
       "20180716  000001.SZ  20180716  8.85  8.90  8.69   8.73       8.88   -0.15   \n",
       "20180713  000001.SZ  20180713  8.92  8.94  8.82   8.88       8.88    0.00   \n",
       "20180712  000001.SZ  20180712  8.60  8.97  8.58   8.88       8.64    0.24   \n",
       "20180711  000001.SZ  20180711  8.76  8.83  8.68   8.78       8.98   -0.20   \n",
       "20180710  000001.SZ  20180710  9.02  9.02  8.89   8.98       9.03   -0.05   \n",
       "20180709  000001.SZ  20180709  8.69  9.03  8.68   9.03       8.66    0.37   \n",
       "20180706  000001.SZ  20180706  8.61  8.78  8.45   8.66       8.60    0.06   \n",
       "20180705  000001.SZ  20180705  8.62  8.73  8.55   8.60       8.61   -0.01   \n",
       "20180704  000001.SZ  20180704  8.63  8.75  8.61   8.61       8.67   -0.06   \n",
       "20180703  000001.SZ  20180703  8.69  8.70  8.45   8.67       8.61    0.06   \n",
       "20180702  000001.SZ  20180702  9.05  9.05  8.55   8.61       9.09   -0.48   \n",
       "\n",
       "          Pct_chg      Volumn       Amount  \n",
       "Date                                        \n",
       "20180718    -0.23   525152.77   460697.377  \n",
       "20180717    -0.11   375356.33   326396.994  \n",
       "20180716    -1.69   689845.58   603427.713  \n",
       "20180713     0.00   603378.21   535401.175  \n",
       "20180712     2.78  1140492.31  1008658.828  \n",
       "20180711    -2.23   851296.70   744765.824  \n",
       "20180710    -0.55   896862.02   803038.965  \n",
       "20180709     4.27  1409954.60  1255007.609  \n",
       "20180706     0.70   988282.69   852071.526  \n",
       "20180705    -0.12   835768.77   722169.579  \n",
       "20180704    -0.69   711153.37   617278.559  \n",
       "20180703     0.70  1274838.57  1096657.033  \n",
       "20180702    -5.28  1315520.13  1158545.868  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dfone.rename(columns=newcolumns, inplace=True)\n",
    "dfone.columns = newcolumns\n",
    "dfone.index = dfone.Date\n",
    "\n",
    "dfone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-02-28'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "a = '20160228'\n",
    "date = datetime.strptime(a, '%Y%m%d').strftime('%Y-%m-%d')\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "20180718   2018-07-18\n",
       "20180717   2018-07-17\n",
       "20180716   2018-07-16\n",
       "20180713   2018-07-13\n",
       "20180712   2018-07-12\n",
       "20180711   2018-07-11\n",
       "20180710   2018-07-10\n",
       "20180709   2018-07-09\n",
       "20180706   2018-07-06\n",
       "20180705   2018-07-05\n",
       "20180704   2018-07-04\n",
       "20180703   2018-07-03\n",
       "20180702   2018-07-02\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.to_datetime(dfone.Date, format='%Y%m%d')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Pre_Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>Pct_chg</th>\n",
       "      <th>Volumn</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180718</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.85</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.72</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>525152.77</td>\n",
       "      <td>460697.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180717</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.72</td>\n",
       "      <td>8.73</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>375356.33</td>\n",
       "      <td>326396.994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-16</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180716</td>\n",
       "      <td>8.85</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.88</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>689845.58</td>\n",
       "      <td>603427.713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-13</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180713</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.94</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>603378.21</td>\n",
       "      <td>535401.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-12</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180712</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.97</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1140492.31</td>\n",
       "      <td>1008658.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-11</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180711</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.83</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.98</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>851296.70</td>\n",
       "      <td>744765.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-10</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180710</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8.98</td>\n",
       "      <td>9.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>896862.02</td>\n",
       "      <td>803038.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180709</td>\n",
       "      <td>8.69</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.68</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.66</td>\n",
       "      <td>0.37</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1409954.60</td>\n",
       "      <td>1255007.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-06</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180706</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.70</td>\n",
       "      <td>988282.69</td>\n",
       "      <td>852071.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180705</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.61</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>835768.77</td>\n",
       "      <td>722169.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180704</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.67</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>711153.37</td>\n",
       "      <td>617278.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180703</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.67</td>\n",
       "      <td>8.61</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1274838.57</td>\n",
       "      <td>1096657.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20180702</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.05</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.09</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-5.28</td>\n",
       "      <td>1315520.13</td>\n",
       "      <td>1158545.868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               symbol      Date  Open  High   Low  Close  Pre_Close  Change  \\\n",
       "Date                                                                          \n",
       "2018-07-18  000001.SZ  20180718  8.75  8.85  8.69   8.70       8.72   -0.02   \n",
       "2018-07-17  000001.SZ  20180717  8.74  8.75  8.66   8.72       8.73   -0.01   \n",
       "2018-07-16  000001.SZ  20180716  8.85  8.90  8.69   8.73       8.88   -0.15   \n",
       "2018-07-13  000001.SZ  20180713  8.92  8.94  8.82   8.88       8.88    0.00   \n",
       "2018-07-12  000001.SZ  20180712  8.60  8.97  8.58   8.88       8.64    0.24   \n",
       "2018-07-11  000001.SZ  20180711  8.76  8.83  8.68   8.78       8.98   -0.20   \n",
       "2018-07-10  000001.SZ  20180710  9.02  9.02  8.89   8.98       9.03   -0.05   \n",
       "2018-07-09  000001.SZ  20180709  8.69  9.03  8.68   9.03       8.66    0.37   \n",
       "2018-07-06  000001.SZ  20180706  8.61  8.78  8.45   8.66       8.60    0.06   \n",
       "2018-07-05  000001.SZ  20180705  8.62  8.73  8.55   8.60       8.61   -0.01   \n",
       "2018-07-04  000001.SZ  20180704  8.63  8.75  8.61   8.61       8.67   -0.06   \n",
       "2018-07-03  000001.SZ  20180703  8.69  8.70  8.45   8.67       8.61    0.06   \n",
       "2018-07-02  000001.SZ  20180702  9.05  9.05  8.55   8.61       9.09   -0.48   \n",
       "\n",
       "            Pct_chg      Volumn       Amount  \n",
       "Date                                          \n",
       "2018-07-18    -0.23   525152.77   460697.377  \n",
       "2018-07-17    -0.11   375356.33   326396.994  \n",
       "2018-07-16    -1.69   689845.58   603427.713  \n",
       "2018-07-13     0.00   603378.21   535401.175  \n",
       "2018-07-12     2.78  1140492.31  1008658.828  \n",
       "2018-07-11    -2.23   851296.70   744765.824  \n",
       "2018-07-10    -0.55   896862.02   803038.965  \n",
       "2018-07-09     4.27  1409954.60  1255007.609  \n",
       "2018-07-06     0.70   988282.69   852071.526  \n",
       "2018-07-05    -0.12   835768.77   722169.579  \n",
       "2018-07-04    -0.69   711153.37   617278.559  \n",
       "2018-07-03     0.70  1274838.57  1096657.033  \n",
       "2018-07-02    -5.28  1315520.13  1158545.868  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dfone.index = pd.to_datetime(dfone.index)\n",
    "dfone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['20180718' '20180717' '20180716' '20180713' '20180712' '20180711'\\n '20180710' '20180709' '20180706' '20180705' '20180704' '20180703'\\n '20180702'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-8cc646281187>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4175\u001b[0m         )\n\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3922\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['20180718' '20180717' '20180716' '20180713' '20180712' '20180711'\\n '20180710' '20180709' '20180706' '20180705' '20180704' '20180703'\\n '20180702'] not found in axis\""
     ]
    }
   ],
   "source": [
    "dfone.drop(dfone.Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplfinance as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.plot(dfone, style='blueskies')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
